Download Notes 10: Qualitative Data

Regression Models
Professor William Greene
Stern School of Business
IOMS Department
Department of Economics
10-1/21
Part 10: Qualitative Data
Statistics and Data Analysis
Part 10 – Qualitative Data
10-2/21
Part 10: Qualitative Data
Modeling Qualitative Data
A Binary Outcome
Yes or No – Bernoulli
 Survey Responses: Preference Scales
 Multiple Choices Such as Brand Choice
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10-3/21
Part 10: Qualitative Data
Binary Outcomes
Did the advertising campaign “work?”
 Will an application be accepted?
 Will a borrower default?
 Will a voter support candidate H?
 Will travelers ride the train?
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10-4/21
Part 10: Qualitative Data
Modeling Fair Isaacs
13,444 Applicants for a Credit Card (November, 1992)
Experiment = A
randomly picked
application.
Let X = 0 if Rejected
Let X = 1 if Accepted
Rejected
10-5/21
Approved
Part 10: Qualitative Data
Modelling The Probability
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10-6/21
Prob[Accept Application] = θ
Prob[Reject Application ] = 1 – θ
Is that all there is?
 Individual 1: Income = $100,000, lived at the
same address for 10 years, owns the home,
no derogatory reports, age 35.
 Individual 2: Income = $15,000, just moved
to the rental apartment, 10 major derogatory
reports, age 22.
 Same value of θ?? Not likely.
Part 10: Qualitative Data
Bernoulli Regression
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Prob[Accept] = θ = a function of
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10-7/21
Age
Income
Derogatory reports
Length at address
Own their home
Looks like regression
Is closely related to regression
A way of handling outcomes (dependent
variables) that are Yes/No, 0/1, etc.
Part 10: Qualitative Data
Binary Logistic Regression
10-8/21
Part 10: Qualitative Data
How To?
It’s not a linear regression model.
 It’s not estimated using least squares.
 How? See more advanced course in
statistics and econometrics
 Why do it here? Recognize this very
common application when you see it.
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10-9/21
Part 10: Qualitative Data
Logistic Regression
10-10/21
Part 10: Qualitative Data
The Question They Are Really Interested In
Of 10,499 people whose application
was accepted, 996 (9.49%)
defaulted on their credit account
(loan). We let X denote the
behavior of a credit card recipient.
X = 0 if no default
X = 1 if default
This is a crucial variable for a
lender. They spend endless
resources trying to learn more
about it.
No Default
10-11/21
Default
Part 10: Qualitative Data
Default Model
Why didn’t mortgage lenders use this
technique in 2000-2007? They didn’t care!
10-12/21
Part 10: Qualitative Data
Application
How to determine if an advertising campaign worked?
A model based on survey data:
Explained variable: Did you buy (or recognize) the
product – Yes/No, 0/1.
Independent variables: (1) Price, (2) Location, (3)…, (4)
Did you see the advertisement? (Yes/No) is 0,1.
The question is then whether effect (4) is “significant.”
This is a candidate for “Binary Logistic Regression”
10-13/21
Part 10: Qualitative Data
Multiple Choices
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Multiple possible outcomes
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10-14/21
Travel mode
Brand choice
Choice among more than two candidates
Television station
Location choice (shopping, living, business)
No natural ordering
Part 10: Qualitative Data
210 Sydney/Melbourne Travelers
10-15/21
Part 10: Qualitative Data
Modeling Multiple Choices
How to combine the information in a model
 The model must recognize that making a
specific choice means not making the other
choices. (Probabilities sum to 1.0.)
 Econometrics II, Spring semester.
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10-16/21
Part 10: Qualitative Data
Ordered Nonquantitative Outcomes
Health satisfaction
 Taste test
 Strength of preferences about
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Legislation
Movie
Fashion
Severity of Injury
 Bond ratings
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10-17/21
Part 10: Qualitative Data
10-18/21
Part 10: Qualitative Data
Bond
Ratings
10-19/21
Part 10: Qualitative Data
Health Satisfaction (HSAT)
Self administered survey: Health Care Satisfaction? (0 – 10)
Continuous Preference Scale
http://w4.stern.nyu.edu/economics/research.cfm?doc_id=7936
Working Paper EC-08: William Greene:Modeling Ordered Choices
10-20/21
Part 10: Qualitative Data
10-21/21
Part 10: Qualitative Data